{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_list = os.listdir('.')\n",
    "data = pd.read_excel(file_list[-3],engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.gitignore',\n",
       " 'h1.ipynb',\n",
       " 'utils.py',\n",
       " '__pycache__',\n",
       " '~$附件1 监测点A空气质量预报基础数据.xlsx',\n",
       " '空气质量预报二次建模.docx',\n",
       " '附件1 监测点A空气质量预报基础数据.xlsx',\n",
       " '附件2 监测点B、C空气质量预报基础数据.xlsx',\n",
       " '附件3 监测点A1、A2、A3空气质量预报基础数据.xlsx']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>模型运行日期</th>\n",
       "      <th>预测时间</th>\n",
       "      <th>地点</th>\n",
       "      <th>近地2米温度（℃）</th>\n",
       "      <th>地表温度（K）</th>\n",
       "      <th>比湿（kg/kg）</th>\n",
       "      <th>湿度（%）</th>\n",
       "      <th>近地10米风速（m/s）</th>\n",
       "      <th>近地10米风向（°）</th>\n",
       "      <th>雨量（mm）</th>\n",
       "      <th>...</th>\n",
       "      <th>潜热通量（W/m²）</th>\n",
       "      <th>长波辐射（W/m²）</th>\n",
       "      <th>短波辐射（W/m²）</th>\n",
       "      <th>地面太阳能辐射（W/m²）</th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
       "      <th>O3小时平均浓度(μg/m³)</th>\n",
       "      <th>CO小时平均浓度(mg/m³)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-23 00:00:00</td>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.8890</td>\n",
       "      <td>304.016</td>\n",
       "      <td>0.018870</td>\n",
       "      <td>66.7409</td>\n",
       "      <td>4.16382</td>\n",
       "      <td>162.577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94818</td>\n",
       "      <td>428.278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.401510</td>\n",
       "      <td>20.9208</td>\n",
       "      <td>8.17336</td>\n",
       "      <td>5.27729</td>\n",
       "      <td>8.78723</td>\n",
       "      <td>0.124491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-23 01:00:00</td>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.8736</td>\n",
       "      <td>303.739</td>\n",
       "      <td>0.017556</td>\n",
       "      <td>62.1551</td>\n",
       "      <td>4.65267</td>\n",
       "      <td>171.978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.14987</td>\n",
       "      <td>427.531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.448340</td>\n",
       "      <td>14.8144</td>\n",
       "      <td>6.49054</td>\n",
       "      <td>4.33106</td>\n",
       "      <td>12.74530</td>\n",
       "      <td>0.109056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-23 02:00:00</td>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.6471</td>\n",
       "      <td>303.419</td>\n",
       "      <td>0.017874</td>\n",
       "      <td>64.1760</td>\n",
       "      <td>4.10031</td>\n",
       "      <td>172.013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01616</td>\n",
       "      <td>427.428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.271610</td>\n",
       "      <td>13.9154</td>\n",
       "      <td>6.86679</td>\n",
       "      <td>4.40045</td>\n",
       "      <td>12.22960</td>\n",
       "      <td>0.105957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-23 03:00:00</td>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.4555</td>\n",
       "      <td>303.419</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>68.7958</td>\n",
       "      <td>2.44317</td>\n",
       "      <td>168.135</td>\n",
       "      <td>0.047224</td>\n",
       "      <td>...</td>\n",
       "      <td>1.89003</td>\n",
       "      <td>442.472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.467429</td>\n",
       "      <td>11.1535</td>\n",
       "      <td>5.25900</td>\n",
       "      <td>3.35261</td>\n",
       "      <td>13.78000</td>\n",
       "      <td>0.101764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-23 04:00:00</td>\n",
       "      <td>监测点A</td>\n",
       "      <td>28.5189</td>\n",
       "      <td>302.987</td>\n",
       "      <td>0.019881</td>\n",
       "      <td>76.5791</td>\n",
       "      <td>2.57759</td>\n",
       "      <td>207.884</td>\n",
       "      <td>8.260020</td>\n",
       "      <td>...</td>\n",
       "      <td>6.53753</td>\n",
       "      <td>458.394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574856</td>\n",
       "      <td>13.9989</td>\n",
       "      <td>6.05979</td>\n",
       "      <td>3.59303</td>\n",
       "      <td>9.96333</td>\n",
       "      <td>0.104536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      模型运行日期                预测时间    地点  近地2米温度（℃）  地表温度（K）  比湿（kg/kg）  \\\n",
       "0 2020-07-23 2020-07-23 00:00:00  监测点A    29.8890  304.016   0.018870   \n",
       "1 2020-07-23 2020-07-23 01:00:00  监测点A    29.8736  303.739   0.017556   \n",
       "2 2020-07-23 2020-07-23 02:00:00  监测点A    29.6471  303.419   0.017874   \n",
       "3 2020-07-23 2020-07-23 03:00:00  监测点A    29.4555  303.419   0.018935   \n",
       "4 2020-07-23 2020-07-23 04:00:00  监测点A    28.5189  302.987   0.019881   \n",
       "\n",
       "     湿度（%）  近地10米风速（m/s）  近地10米风向（°）    雨量（mm）  ...  潜热通量（W/m²）  长波辐射（W/m²）  \\\n",
       "0  66.7409       4.16382     162.577  0.000000  ...     0.94818     428.278   \n",
       "1  62.1551       4.65267     171.978  0.000000  ...     1.14987     427.531   \n",
       "2  64.1760       4.10031     172.013  0.000000  ...     1.01616     427.428   \n",
       "3  68.7958       2.44317     168.135  0.047224  ...     1.89003     442.472   \n",
       "4  76.5791       2.57759     207.884  8.260020  ...     6.53753     458.394   \n",
       "\n",
       "   短波辐射（W/m²）  地面太阳能辐射（W/m²）  SO2小时平均浓度(μg/m³)  NO2小时平均浓度(μg/m³)  \\\n",
       "0         0.0            0.0          2.401510           20.9208   \n",
       "1         0.0            0.0          1.448340           14.8144   \n",
       "2         0.0            0.0          1.271610           13.9154   \n",
       "3         0.0            0.0          0.467429           11.1535   \n",
       "4         0.0            0.0          0.574856           13.9989   \n",
       "\n",
       "   PM10小时平均浓度(μg/m³)  PM2.5小时平均浓度(μg/m³)  O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "0            8.17336             5.27729          8.78723         0.124491  \n",
       "1            6.49054             4.33106         12.74530         0.109056  \n",
       "2            6.86679             4.40045         12.22960         0.105957  \n",
       "3            5.25900             3.35261         13.78000         0.101764  \n",
       "4            6.05979             3.59303          9.96333         0.104536  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25416 entries, 0 to 25415\n",
      "Data columns (total 24 columns):\n",
      "模型运行日期                25416 non-null datetime64[ns]\n",
      "预测时间                  25416 non-null datetime64[ns]\n",
      "地点                    25416 non-null object\n",
      "近地2米温度（℃）             25416 non-null float64\n",
      "地表温度（K）               25416 non-null float64\n",
      "比湿（kg/kg）             25416 non-null float64\n",
      "湿度（%）                 25416 non-null float64\n",
      "近地10米风速（m/s）          25416 non-null float64\n",
      "近地10米风向（°）            25416 non-null float64\n",
      "雨量（mm）                25416 non-null float64\n",
      "云量                    25416 non-null float64\n",
      "边界层高度（m）              25416 non-null float64\n",
      "大气压（Kpa）              25416 non-null float64\n",
      "感热通量（W/m²）            25416 non-null float64\n",
      "潜热通量（W/m²）            25416 non-null float64\n",
      "长波辐射（W/m²）            25416 non-null float64\n",
      "短波辐射（W/m²）            25416 non-null float64\n",
      "地面太阳能辐射（W/m²）         25416 non-null float64\n",
      "SO2小时平均浓度(μg/m³)      25416 non-null float64\n",
      "NO2小时平均浓度(μg/m³)      25416 non-null float64\n",
      "PM10小时平均浓度(μg/m³)     25416 non-null float64\n",
      "PM2.5小时平均浓度(μg/m³)    25416 non-null float64\n",
      "O3小时平均浓度(μg/m³)       25416 non-null float64\n",
      "CO小时平均浓度(mg/m³)       25416 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(21), object(1)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>近地2米温度（℃）</th>\n",
       "      <th>地表温度（K）</th>\n",
       "      <th>比湿（kg/kg）</th>\n",
       "      <th>湿度（%）</th>\n",
       "      <th>近地10米风速（m/s）</th>\n",
       "      <th>近地10米风向（°）</th>\n",
       "      <th>雨量（mm）</th>\n",
       "      <th>云量</th>\n",
       "      <th>边界层高度（m）</th>\n",
       "      <th>大气压（Kpa）</th>\n",
       "      <th>...</th>\n",
       "      <th>潜热通量（W/m²）</th>\n",
       "      <th>长波辐射（W/m²）</th>\n",
       "      <th>短波辐射（W/m²）</th>\n",
       "      <th>地面太阳能辐射（W/m²）</th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
       "      <th>O3小时平均浓度(μg/m³)</th>\n",
       "      <th>CO小时平均浓度(mg/m³)</th>\n",
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       "      <th>count</th>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>25416.000000</td>\n",
       "      <td>2.541600e+04</td>\n",
       "      <td>25416.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.941367</td>\n",
       "      <td>301.906307</td>\n",
       "      <td>0.013027</td>\n",
       "      <td>59.271910</td>\n",
       "      <td>3.927604</td>\n",
       "      <td>123.315270</td>\n",
       "      <td>0.391002</td>\n",
       "      <td>0.460008</td>\n",
       "      <td>674.963830</td>\n",
       "      <td>101.190651</td>\n",
       "      <td>...</td>\n",
       "      <td>16.962561</td>\n",
       "      <td>395.170426</td>\n",
       "      <td>215.264752</td>\n",
       "      <td>258.667851</td>\n",
       "      <td>8.095342</td>\n",
       "      <td>57.976588</td>\n",
       "      <td>33.951973</td>\n",
       "      <td>27.052738</td>\n",
       "      <td>3.289282e+01</td>\n",
       "      <td>0.298747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.972074</td>\n",
       "      <td>8.613403</td>\n",
       "      <td>0.005130</td>\n",
       "      <td>16.499209</td>\n",
       "      <td>1.696326</td>\n",
       "      <td>79.249468</td>\n",
       "      <td>1.991600</td>\n",
       "      <td>0.326640</td>\n",
       "      <td>450.299871</td>\n",
       "      <td>0.651010</td>\n",
       "      <td>...</td>\n",
       "      <td>19.651134</td>\n",
       "      <td>49.883333</td>\n",
       "      <td>276.857248</td>\n",
       "      <td>332.679032</td>\n",
       "      <td>7.977947</td>\n",
       "      <td>34.019993</td>\n",
       "      <td>28.272897</td>\n",
       "      <td>24.408726</td>\n",
       "      <td>3.549418e+01</td>\n",
       "      <td>0.231951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.413970</td>\n",
       "      <td>275.448000</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>8.546440</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.774900</td>\n",
       "      <td>99.664800</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.089550</td>\n",
       "      <td>230.671000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>7.685690</td>\n",
       "      <td>0.538817</td>\n",
       "      <td>0.410555</td>\n",
       "      <td>2.052830e-14</td>\n",
       "      <td>0.089578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.211475</td>\n",
       "      <td>296.083750</td>\n",
       "      <td>0.009315</td>\n",
       "      <td>47.224525</td>\n",
       "      <td>2.711615</td>\n",
       "      <td>56.523125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150826</td>\n",
       "      <td>330.467500</td>\n",
       "      <td>100.631000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.073412</td>\n",
       "      <td>360.149500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.695410</td>\n",
       "      <td>31.543275</td>\n",
       "      <td>11.532400</td>\n",
       "      <td>7.621767</td>\n",
       "      <td>4.135275e+00</td>\n",
       "      <td>0.163849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25.947200</td>\n",
       "      <td>301.986000</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>59.458100</td>\n",
       "      <td>3.834030</td>\n",
       "      <td>130.049500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.439354</td>\n",
       "      <td>549.772000</td>\n",
       "      <td>101.190000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.617565</td>\n",
       "      <td>407.791000</td>\n",
       "      <td>31.445800</td>\n",
       "      <td>37.786050</td>\n",
       "      <td>5.527160</td>\n",
       "      <td>50.482150</td>\n",
       "      <td>25.355650</td>\n",
       "      <td>17.991850</td>\n",
       "      <td>2.122600e+01</td>\n",
       "      <td>0.229687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.278250</td>\n",
       "      <td>307.547000</td>\n",
       "      <td>0.017701</td>\n",
       "      <td>72.739450</td>\n",
       "      <td>5.059253</td>\n",
       "      <td>172.048250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.774837</td>\n",
       "      <td>969.257750</td>\n",
       "      <td>101.689250</td>\n",
       "      <td>...</td>\n",
       "      <td>30.767725</td>\n",
       "      <td>435.506750</td>\n",
       "      <td>422.042750</td>\n",
       "      <td>507.138000</td>\n",
       "      <td>10.960800</td>\n",
       "      <td>78.987875</td>\n",
       "      <td>49.329875</td>\n",
       "      <td>41.128100</td>\n",
       "      <td>5.217420e+01</td>\n",
       "      <td>0.349102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>37.471400</td>\n",
       "      <td>323.987000</td>\n",
       "      <td>0.023138</td>\n",
       "      <td>99.149200</td>\n",
       "      <td>12.712100</td>\n",
       "      <td>359.995000</td>\n",
       "      <td>86.203000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2465.210000</td>\n",
       "      <td>102.925000</td>\n",
       "      <td>...</td>\n",
       "      <td>77.258600</td>\n",
       "      <td>491.217000</td>\n",
       "      <td>889.015000</td>\n",
       "      <td>1068.260000</td>\n",
       "      <td>134.948000</td>\n",
       "      <td>610.844000</td>\n",
       "      <td>253.909000</td>\n",
       "      <td>190.873000</td>\n",
       "      <td>4.151800e+02</td>\n",
       "      <td>3.214280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          近地2米温度（℃）       地表温度（K）     比湿（kg/kg）         湿度（%）  近地10米风速（m/s）  \\\n",
       "count  25416.000000  25416.000000  25416.000000  25416.000000  25416.000000   \n",
       "mean      24.941367    301.906307      0.013027     59.271910      3.927604   \n",
       "std        5.972074      8.613403      0.005130     16.499209      1.696326   \n",
       "min        3.413970    275.448000      0.000647      8.546440      0.012006   \n",
       "25%       21.211475    296.083750      0.009315     47.224525      2.711615   \n",
       "50%       25.947200    301.986000      0.013289     59.458100      3.834030   \n",
       "75%       29.278250    307.547000      0.017701     72.739450      5.059253   \n",
       "max       37.471400    323.987000      0.023138     99.149200     12.712100   \n",
       "\n",
       "         近地10米风向（°）        雨量（mm）            云量      边界层高度（m）      大气压（Kpa）  \\\n",
       "count  25416.000000  25416.000000  25416.000000  25416.000000  25416.000000   \n",
       "mean     123.315270      0.391002      0.460008    674.963830    101.190651   \n",
       "std       79.249468      1.991600      0.326640    450.299871      0.651010   \n",
       "min        0.002380      0.000000      0.000000     19.774900     99.664800   \n",
       "25%       56.523125      0.000000      0.150826    330.467500    100.631000   \n",
       "50%      130.049500      0.000000      0.439354    549.772000    101.190000   \n",
       "75%      172.048250      0.000000      0.774837    969.257750    101.689250   \n",
       "max      359.995000     86.203000      1.000000   2465.210000    102.925000   \n",
       "\n",
       "       ...    潜热通量（W/m²）    长波辐射（W/m²）    短波辐射（W/m²）  地面太阳能辐射（W/m²）  \\\n",
       "count  ...  25416.000000  25416.000000  25416.000000   25416.000000   \n",
       "mean   ...     16.962561    395.170426    215.264752     258.667851   \n",
       "std    ...     19.651134     49.883333    276.857248     332.679032   \n",
       "min    ...     -2.089550    230.671000      0.000000       0.000000   \n",
       "25%    ...      1.073412    360.149500      0.000000       0.000000   \n",
       "50%    ...      6.617565    407.791000     31.445800      37.786050   \n",
       "75%    ...     30.767725    435.506750    422.042750     507.138000   \n",
       "max    ...     77.258600    491.217000    889.015000    1068.260000   \n",
       "\n",
       "       SO2小时平均浓度(μg/m³)  NO2小时平均浓度(μg/m³)  PM10小时平均浓度(μg/m³)  \\\n",
       "count      25416.000000      25416.000000       25416.000000   \n",
       "mean           8.095342         57.976588          33.951973   \n",
       "std            7.977947         34.019993          28.272897   \n",
       "min            0.003020          7.685690           0.538817   \n",
       "25%            2.695410         31.543275          11.532400   \n",
       "50%            5.527160         50.482150          25.355650   \n",
       "75%           10.960800         78.987875          49.329875   \n",
       "max          134.948000        610.844000         253.909000   \n",
       "\n",
       "       PM2.5小时平均浓度(μg/m³)  O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "count        25416.000000     2.541600e+04     25416.000000  \n",
       "mean            27.052738     3.289282e+01         0.298747  \n",
       "std             24.408726     3.549418e+01         0.231951  \n",
       "min              0.410555     2.052830e-14         0.089578  \n",
       "25%              7.621767     4.135275e+00         0.163849  \n",
       "50%             17.991850     2.122600e+01         0.229687  \n",
       "75%             41.128100     5.217420e+01         0.349102  \n",
       "max            190.873000     4.151800e+02         3.214280  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 178 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 178 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 178 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 5760x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "def save_fig(figname):\n",
    "    img_path = './img/' + figname  + '.svg'\n",
    "    plt.savefig(img_path)\n",
    "import os\n",
    "os.makedirs('./img',exist_ok=True)\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(80,30))\n",
    "data.boxplot()\n",
    "save_fig('h1-boxplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_excel(file_list[-3],engine='openpyxl',index_col=0)\n",
    "data3=data2.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地点</th>\n",
       "      <th>近地2米温度（℃）</th>\n",
       "      <th>地表温度（K）</th>\n",
       "      <th>比湿（kg/kg）</th>\n",
       "      <th>湿度（%）</th>\n",
       "      <th>近地10米风速（m/s）</th>\n",
       "      <th>近地10米风向（°）</th>\n",
       "      <th>雨量（mm）</th>\n",
       "      <th>云量</th>\n",
       "      <th>边界层高度（m）</th>\n",
       "      <th>...</th>\n",
       "      <th>潜热通量（W/m²）</th>\n",
       "      <th>长波辐射（W/m²）</th>\n",
       "      <th>短波辐射（W/m²）</th>\n",
       "      <th>地面太阳能辐射（W/m²）</th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
       "      <th>O3小时平均浓度(μg/m³)</th>\n",
       "      <th>CO小时平均浓度(mg/m³)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>模型运行日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.8890</td>\n",
       "      <td>304.016</td>\n",
       "      <td>0.018870</td>\n",
       "      <td>66.7409</td>\n",
       "      <td>4.16382</td>\n",
       "      <td>162.577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038201</td>\n",
       "      <td>769.903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94818</td>\n",
       "      <td>428.278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.401510</td>\n",
       "      <td>20.9208</td>\n",
       "      <td>8.17336</td>\n",
       "      <td>5.27729</td>\n",
       "      <td>8.78723</td>\n",
       "      <td>0.124491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.8736</td>\n",
       "      <td>303.739</td>\n",
       "      <td>0.017556</td>\n",
       "      <td>62.1551</td>\n",
       "      <td>4.65267</td>\n",
       "      <td>171.978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058892</td>\n",
       "      <td>682.085</td>\n",
       "      <td>...</td>\n",
       "      <td>1.14987</td>\n",
       "      <td>427.531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.448340</td>\n",
       "      <td>14.8144</td>\n",
       "      <td>6.49054</td>\n",
       "      <td>4.33106</td>\n",
       "      <td>12.74530</td>\n",
       "      <td>0.109056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.6471</td>\n",
       "      <td>303.419</td>\n",
       "      <td>0.017874</td>\n",
       "      <td>64.1760</td>\n",
       "      <td>4.10031</td>\n",
       "      <td>172.013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065039</td>\n",
       "      <td>627.078</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01616</td>\n",
       "      <td>427.428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.271610</td>\n",
       "      <td>13.9154</td>\n",
       "      <td>6.86679</td>\n",
       "      <td>4.40045</td>\n",
       "      <td>12.22960</td>\n",
       "      <td>0.105957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.4555</td>\n",
       "      <td>303.419</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>68.7958</td>\n",
       "      <td>2.44317</td>\n",
       "      <td>168.135</td>\n",
       "      <td>0.047224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>636.497</td>\n",
       "      <td>...</td>\n",
       "      <td>1.89003</td>\n",
       "      <td>442.472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.467429</td>\n",
       "      <td>11.1535</td>\n",
       "      <td>5.25900</td>\n",
       "      <td>3.35261</td>\n",
       "      <td>13.78000</td>\n",
       "      <td>0.101764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>28.5189</td>\n",
       "      <td>302.987</td>\n",
       "      <td>0.019881</td>\n",
       "      <td>76.5791</td>\n",
       "      <td>2.57759</td>\n",
       "      <td>207.884</td>\n",
       "      <td>8.260020</td>\n",
       "      <td>0.935074</td>\n",
       "      <td>490.162</td>\n",
       "      <td>...</td>\n",
       "      <td>6.53753</td>\n",
       "      <td>458.394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574856</td>\n",
       "      <td>13.9989</td>\n",
       "      <td>6.05979</td>\n",
       "      <td>3.59303</td>\n",
       "      <td>9.96333</td>\n",
       "      <td>0.104536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              地点  近地2米温度（℃）  地表温度（K）  比湿（kg/kg）    湿度（%）  近地10米风速（m/s）  \\\n",
       "模型运行日期                                                                   \n",
       "2020-07-23  监测点A    29.8890  304.016   0.018870  66.7409       4.16382   \n",
       "2020-07-23  监测点A    29.8736  303.739   0.017556  62.1551       4.65267   \n",
       "2020-07-23  监测点A    29.6471  303.419   0.017874  64.1760       4.10031   \n",
       "2020-07-23  监测点A    29.4555  303.419   0.018935  68.7958       2.44317   \n",
       "2020-07-23  监测点A    28.5189  302.987   0.019881  76.5791       2.57759   \n",
       "\n",
       "            近地10米风向（°）    雨量（mm）        云量  边界层高度（m）  ...  潜热通量（W/m²）  \\\n",
       "模型运行日期                                                ...               \n",
       "2020-07-23     162.577  0.000000  0.038201   769.903  ...     0.94818   \n",
       "2020-07-23     171.978  0.000000  0.058892   682.085  ...     1.14987   \n",
       "2020-07-23     172.013  0.000000  0.065039   627.078  ...     1.01616   \n",
       "2020-07-23     168.135  0.047224  1.000000   636.497  ...     1.89003   \n",
       "2020-07-23     207.884  8.260020  0.935074   490.162  ...     6.53753   \n",
       "\n",
       "            长波辐射（W/m²）  短波辐射（W/m²）  地面太阳能辐射（W/m²）  SO2小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                                \n",
       "2020-07-23     428.278         0.0            0.0          2.401510   \n",
       "2020-07-23     427.531         0.0            0.0          1.448340   \n",
       "2020-07-23     427.428         0.0            0.0          1.271610   \n",
       "2020-07-23     442.472         0.0            0.0          0.467429   \n",
       "2020-07-23     458.394         0.0            0.0          0.574856   \n",
       "\n",
       "            NO2小时平均浓度(μg/m³)  PM10小时平均浓度(μg/m³)  PM2.5小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                                \n",
       "2020-07-23           20.9208            8.17336             5.27729   \n",
       "2020-07-23           14.8144            6.49054             4.33106   \n",
       "2020-07-23           13.9154            6.86679             4.40045   \n",
       "2020-07-23           11.1535            5.25900             3.35261   \n",
       "2020-07-23           13.9989            6.05979             3.59303   \n",
       "\n",
       "            O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "模型运行日期                                        \n",
       "2020-07-23          8.78723         0.124491  \n",
       "2020-07-23         12.74530         0.109056  \n",
       "2020-07-23         12.22960         0.105957  \n",
       "2020-07-23         13.78000         0.101764  \n",
       "2020-07-23          9.96333         0.104536  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地点</th>\n",
       "      <th>近地2米温度（℃）</th>\n",
       "      <th>地表温度（K）</th>\n",
       "      <th>比湿（kg/kg）</th>\n",
       "      <th>湿度（%）</th>\n",
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       "      <th>云量</th>\n",
       "      <th>边界层高度（m）</th>\n",
       "      <th>...</th>\n",
       "      <th>潜热通量（W/m²）</th>\n",
       "      <th>长波辐射（W/m²）</th>\n",
       "      <th>短波辐射（W/m²）</th>\n",
       "      <th>地面太阳能辐射（W/m²）</th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
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       "      <th>CO小时平均浓度(mg/m³)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>模型运行日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.8509</td>\n",
       "      <td>304.037</td>\n",
       "      <td>0.018892</td>\n",
       "      <td>66.7419</td>\n",
       "      <td>5.60695</td>\n",
       "      <td>229.9170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386271</td>\n",
       "      <td>420.672</td>\n",
       "      <td>...</td>\n",
       "      <td>1.574010</td>\n",
       "      <td>433.938</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.93113</td>\n",
       "      <td>23.0395</td>\n",
       "      <td>9.34302</td>\n",
       "      <td>7.04022</td>\n",
       "      <td>44.9474</td>\n",
       "      <td>0.149888</td>\n",
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       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.4782</td>\n",
       "      <td>303.592</td>\n",
       "      <td>0.018897</td>\n",
       "      <td>68.3008</td>\n",
       "      <td>4.85994</td>\n",
       "      <td>236.2350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.412845</td>\n",
       "      <td>395.553</td>\n",
       "      <td>...</td>\n",
       "      <td>1.384910</td>\n",
       "      <td>432.131</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.40358</td>\n",
       "      <td>17.6013</td>\n",
       "      <td>8.58349</td>\n",
       "      <td>6.32526</td>\n",
       "      <td>46.2421</td>\n",
       "      <td>0.128529</td>\n",
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       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>29.0641</td>\n",
       "      <td>303.102</td>\n",
       "      <td>0.019865</td>\n",
       "      <td>73.6459</td>\n",
       "      <td>4.95854</td>\n",
       "      <td>232.3990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.481305</td>\n",
       "      <td>347.324</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096060</td>\n",
       "      <td>430.986</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.29327</td>\n",
       "      <td>17.7531</td>\n",
       "      <td>9.04252</td>\n",
       "      <td>6.63679</td>\n",
       "      <td>42.6007</td>\n",
       "      <td>0.124948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>28.8226</td>\n",
       "      <td>302.717</td>\n",
       "      <td>0.019876</td>\n",
       "      <td>74.7762</td>\n",
       "      <td>5.05084</td>\n",
       "      <td>236.3480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.453799</td>\n",
       "      <td>306.040</td>\n",
       "      <td>...</td>\n",
       "      <td>1.024380</td>\n",
       "      <td>429.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.62926</td>\n",
       "      <td>20.5377</td>\n",
       "      <td>10.75420</td>\n",
       "      <td>7.97859</td>\n",
       "      <td>39.8489</td>\n",
       "      <td>0.127692</td>\n",
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       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>28.5922</td>\n",
       "      <td>302.411</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>74.5120</td>\n",
       "      <td>3.94807</td>\n",
       "      <td>241.3490</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.405452</td>\n",
       "      <td>236.057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.905658</td>\n",
       "      <td>426.863</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.03218</td>\n",
       "      <td>25.4339</td>\n",
       "      <td>12.88870</td>\n",
       "      <td>9.46248</td>\n",
       "      <td>35.6610</td>\n",
       "      <td>0.132719</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>36.3235</td>\n",
       "      <td>319.673</td>\n",
       "      <td>0.012811</td>\n",
       "      <td>30.4059</td>\n",
       "      <td>3.56392</td>\n",
       "      <td>69.3560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115842</td>\n",
       "      <td>2356.460</td>\n",
       "      <td>...</td>\n",
       "      <td>48.999000</td>\n",
       "      <td>442.212</td>\n",
       "      <td>522.7900</td>\n",
       "      <td>628.1980</td>\n",
       "      <td>4.21637</td>\n",
       "      <td>47.3763</td>\n",
       "      <td>57.68200</td>\n",
       "      <td>51.85880</td>\n",
       "      <td>162.5870</td>\n",
       "      <td>0.413631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>36.5456</td>\n",
       "      <td>316.927</td>\n",
       "      <td>0.012172</td>\n",
       "      <td>28.5121</td>\n",
       "      <td>3.40959</td>\n",
       "      <td>74.4805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.561608</td>\n",
       "      <td>2381.560</td>\n",
       "      <td>...</td>\n",
       "      <td>35.669900</td>\n",
       "      <td>443.759</td>\n",
       "      <td>323.7770</td>\n",
       "      <td>389.0590</td>\n",
       "      <td>2.76253</td>\n",
       "      <td>34.3521</td>\n",
       "      <td>43.42040</td>\n",
       "      <td>38.38750</td>\n",
       "      <td>152.3120</td>\n",
       "      <td>0.327249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>34.2888</td>\n",
       "      <td>311.070</td>\n",
       "      <td>0.015643</td>\n",
       "      <td>42.0497</td>\n",
       "      <td>7.56557</td>\n",
       "      <td>185.0370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.874501</td>\n",
       "      <td>666.393</td>\n",
       "      <td>...</td>\n",
       "      <td>12.001800</td>\n",
       "      <td>476.424</td>\n",
       "      <td>25.9688</td>\n",
       "      <td>31.2047</td>\n",
       "      <td>2.75568</td>\n",
       "      <td>34.5332</td>\n",
       "      <td>43.02690</td>\n",
       "      <td>37.72850</td>\n",
       "      <td>130.1490</td>\n",
       "      <td>0.304765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>33.5478</td>\n",
       "      <td>309.364</td>\n",
       "      <td>0.016211</td>\n",
       "      <td>45.6232</td>\n",
       "      <td>3.87447</td>\n",
       "      <td>210.1590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.608190</td>\n",
       "      <td>977.275</td>\n",
       "      <td>...</td>\n",
       "      <td>2.241950</td>\n",
       "      <td>443.416</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.00893</td>\n",
       "      <td>33.0516</td>\n",
       "      <td>41.34820</td>\n",
       "      <td>36.25500</td>\n",
       "      <td>123.6750</td>\n",
       "      <td>0.318108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>监测点A</td>\n",
       "      <td>32.9241</td>\n",
       "      <td>308.183</td>\n",
       "      <td>0.017280</td>\n",
       "      <td>50.5625</td>\n",
       "      <td>3.82616</td>\n",
       "      <td>225.9420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692192</td>\n",
       "      <td>884.650</td>\n",
       "      <td>...</td>\n",
       "      <td>1.993900</td>\n",
       "      <td>444.070</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.83411</td>\n",
       "      <td>25.6230</td>\n",
       "      <td>47.09830</td>\n",
       "      <td>41.33110</td>\n",
       "      <td>116.2310</td>\n",
       "      <td>0.299503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              地点  近地2米温度（℃）  地表温度（K）  比湿（kg/kg）    湿度（%）  近地10米风速（m/s）  \\\n",
       "模型运行日期                                                                   \n",
       "2020-08-25  监测点A    29.8509  304.037   0.018892  66.7419       5.60695   \n",
       "2020-08-25  监测点A    29.4782  303.592   0.018897  68.3008       4.85994   \n",
       "2020-08-25  监测点A    29.0641  303.102   0.019865  73.6459       4.95854   \n",
       "2020-08-25  监测点A    28.8226  302.717   0.019876  74.7762       5.05084   \n",
       "2020-08-25  监测点A    28.5922  302.411   0.019527  74.5120       3.94807   \n",
       "...          ...        ...      ...        ...      ...           ...   \n",
       "2020-08-28  监测点A    36.3235  319.673   0.012811  30.4059       3.56392   \n",
       "2020-08-28  监测点A    36.5456  316.927   0.012172  28.5121       3.40959   \n",
       "2020-08-28  监测点A    34.2888  311.070   0.015643  42.0497       7.56557   \n",
       "2020-08-28  监测点A    33.5478  309.364   0.016211  45.6232       3.87447   \n",
       "2020-08-28  监测点A    32.9241  308.183   0.017280  50.5625       3.82616   \n",
       "\n",
       "            近地10米风向（°）  雨量（mm）        云量  边界层高度（m）  ...  潜热通量（W/m²）  \\\n",
       "模型运行日期                                              ...               \n",
       "2020-08-25    229.9170     0.0  0.386271   420.672  ...    1.574010   \n",
       "2020-08-25    236.2350     0.0  0.412845   395.553  ...    1.384910   \n",
       "2020-08-25    232.3990     0.0  0.481305   347.324  ...    1.096060   \n",
       "2020-08-25    236.3480     0.0  0.453799   306.040  ...    1.024380   \n",
       "2020-08-25    241.3490     0.0  0.405452   236.057  ...    0.905658   \n",
       "...                ...     ...       ...       ...  ...         ...   \n",
       "2020-08-28     69.3560     0.0  0.115842  2356.460  ...   48.999000   \n",
       "2020-08-28     74.4805     0.0  0.561608  2381.560  ...   35.669900   \n",
       "2020-08-28    185.0370     0.0  0.874501   666.393  ...   12.001800   \n",
       "2020-08-28    210.1590     0.0  0.608190   977.275  ...    2.241950   \n",
       "2020-08-28    225.9420     0.0  0.692192   884.650  ...    1.993900   \n",
       "\n",
       "            长波辐射（W/m²）  短波辐射（W/m²）  地面太阳能辐射（W/m²）  SO2小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                                \n",
       "2020-08-25     433.938      0.0000         0.0000           2.93113   \n",
       "2020-08-25     432.131      0.0000         0.0000           1.40358   \n",
       "2020-08-25     430.986      0.0000         0.0000           1.29327   \n",
       "2020-08-25     429.025      0.0000         0.0000           1.62926   \n",
       "2020-08-25     426.863      0.0000         0.0000           2.03218   \n",
       "...                ...         ...            ...               ...   \n",
       "2020-08-28     442.212    522.7900       628.1980           4.21637   \n",
       "2020-08-28     443.759    323.7770       389.0590           2.76253   \n",
       "2020-08-28     476.424     25.9688        31.2047           2.75568   \n",
       "2020-08-28     443.416      0.0000         0.0000           2.00893   \n",
       "2020-08-28     444.070      0.0000         0.0000           2.83411   \n",
       "\n",
       "            NO2小时平均浓度(μg/m³)  PM10小时平均浓度(μg/m³)  PM2.5小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                                \n",
       "2020-08-25           23.0395            9.34302             7.04022   \n",
       "2020-08-25           17.6013            8.58349             6.32526   \n",
       "2020-08-25           17.7531            9.04252             6.63679   \n",
       "2020-08-25           20.5377           10.75420             7.97859   \n",
       "2020-08-25           25.4339           12.88870             9.46248   \n",
       "...                      ...                ...                 ...   \n",
       "2020-08-28           47.3763           57.68200            51.85880   \n",
       "2020-08-28           34.3521           43.42040            38.38750   \n",
       "2020-08-28           34.5332           43.02690            37.72850   \n",
       "2020-08-28           33.0516           41.34820            36.25500   \n",
       "2020-08-28           25.6230           47.09830            41.33110   \n",
       "\n",
       "            O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "模型运行日期                                        \n",
       "2020-08-25          44.9474         0.149888  \n",
       "2020-08-25          46.2421         0.128529  \n",
       "2020-08-25          42.6007         0.124948  \n",
       "2020-08-25          39.8489         0.127692  \n",
       "2020-08-25          35.6610         0.132719  \n",
       "...                     ...              ...  \n",
       "2020-08-28         162.5870         0.413631  \n",
       "2020-08-28         152.3120         0.327249  \n",
       "2020-08-28         130.1490         0.304765  \n",
       "2020-08-28         123.6750         0.318108  \n",
       "2020-08-28         116.2310         0.299503  \n",
       "\n",
       "[288 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3['2020-08-25':'2020-08-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_for_answer=data3.iloc[:,-6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
       "      <th>O3小时平均浓度(μg/m³)</th>\n",
       "      <th>CO小时平均浓度(mg/m³)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>模型运行日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>2.401510</td>\n",
       "      <td>20.9208</td>\n",
       "      <td>8.17336</td>\n",
       "      <td>5.27729</td>\n",
       "      <td>8.787230</td>\n",
       "      <td>0.124491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>1.448340</td>\n",
       "      <td>14.8144</td>\n",
       "      <td>6.49054</td>\n",
       "      <td>4.33106</td>\n",
       "      <td>12.745300</td>\n",
       "      <td>0.109056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>1.271610</td>\n",
       "      <td>13.9154</td>\n",
       "      <td>6.86679</td>\n",
       "      <td>4.40045</td>\n",
       "      <td>12.229600</td>\n",
       "      <td>0.105957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>0.467429</td>\n",
       "      <td>11.1535</td>\n",
       "      <td>5.25900</td>\n",
       "      <td>3.35261</td>\n",
       "      <td>13.780000</td>\n",
       "      <td>0.101764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>0.574856</td>\n",
       "      <td>13.9989</td>\n",
       "      <td>6.05979</td>\n",
       "      <td>3.59303</td>\n",
       "      <td>9.963330</td>\n",
       "      <td>0.104536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>2.679800</td>\n",
       "      <td>44.8120</td>\n",
       "      <td>8.92721</td>\n",
       "      <td>5.66833</td>\n",
       "      <td>0.333226</td>\n",
       "      <td>0.224216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>2.937120</td>\n",
       "      <td>41.3621</td>\n",
       "      <td>9.00740</td>\n",
       "      <td>5.67797</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.233528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>3.084680</td>\n",
       "      <td>41.5508</td>\n",
       "      <td>9.13525</td>\n",
       "      <td>5.76356</td>\n",
       "      <td>0.024794</td>\n",
       "      <td>0.237719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>4.003400</td>\n",
       "      <td>42.4011</td>\n",
       "      <td>8.24599</td>\n",
       "      <td>5.43220</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.260096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>4.785530</td>\n",
       "      <td>42.6422</td>\n",
       "      <td>7.40284</td>\n",
       "      <td>5.14862</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.268417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25416 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SO2小时平均浓度(μg/m³)  NO2小时平均浓度(μg/m³)  PM10小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                              \n",
       "2020-07-23          2.401510           20.9208            8.17336   \n",
       "2020-07-23          1.448340           14.8144            6.49054   \n",
       "2020-07-23          1.271610           13.9154            6.86679   \n",
       "2020-07-23          0.467429           11.1535            5.25900   \n",
       "2020-07-23          0.574856           13.9989            6.05979   \n",
       "...                      ...               ...                ...   \n",
       "2021-07-13          2.679800           44.8120            8.92721   \n",
       "2021-07-13          2.937120           41.3621            9.00740   \n",
       "2021-07-13          3.084680           41.5508            9.13525   \n",
       "2021-07-13          4.003400           42.4011            8.24599   \n",
       "2021-07-13          4.785530           42.6422            7.40284   \n",
       "\n",
       "            PM2.5小时平均浓度(μg/m³)  O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "模型运行日期                                                            \n",
       "2020-07-23             5.27729         8.787230         0.124491  \n",
       "2020-07-23             4.33106        12.745300         0.109056  \n",
       "2020-07-23             4.40045        12.229600         0.105957  \n",
       "2020-07-23             3.35261        13.780000         0.101764  \n",
       "2020-07-23             3.59303         9.963330         0.104536  \n",
       "...                        ...              ...              ...  \n",
       "2021-07-13             5.66833         0.333226         0.224216  \n",
       "2021-07-13             5.67797         0.007336         0.233528  \n",
       "2021-07-13             5.76356         0.024794         0.237719  \n",
       "2021-07-13             5.43220         0.000928         0.260096  \n",
       "2021-07-13             5.14862         0.001013         0.268417  \n",
       "\n",
       "[25416 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 179 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "data_for_answer.boxplot()\n",
    "save_fig('h1-6-features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_list = os.listdir('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.gitignore',\n",
       " 'h1.ipynb',\n",
       " 'iaqi.xls',\n",
       " 'img',\n",
       " 'utils.py',\n",
       " '__pycache__',\n",
       " '~$附件1 监测点A空气质量预报基础数据.xlsx',\n",
       " '空气质量预报二次建模.docx',\n",
       " '附件1 监测点A空气质量预报基础数据.xlsx',\n",
       " '附件2 监测点B、C空气质量预报基础数据.xlsx',\n",
       " '附件3 监测点A1、A2、A3空气质量预报基础数据.xlsx']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_table=pd.read_excel('iaqi.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "      <th>0</th>\n",
       "      <th>空气质量分指数（IAQI）</th>\n",
       "      <th>0.1</th>\n",
       "      <th>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>300</th>\n",
       "      <th>400</th>\n",
       "      <th>500</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>一氧化碳（CO）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>二氧化硫（SO2）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>2620</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>二氧化氮（NO2）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>750</td>\n",
       "      <td>940</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>臭氧（O3）最大8小时滑动平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>500</td>\n",
       "      <td>600</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>粒径小于等于2.5颗粒物（PM2.5）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0              空气质量分指数（IAQI）  0.1     50    100    150    200     300  \\\n",
       "0  1.0             一氧化碳（CO）24小时平均  0.0    2.0    4.0   14.0   24.0    36.0   \n",
       "1  2.0            二氧化硫（SO2）24小时平均  0.0   50.0  150.0  475.0  800.0  1600.0   \n",
       "2  3.0            二氧化氮（NO2）24小时平均  0.0   40.0   80.0  180.0  280.0   565.0   \n",
       "3  4.0            臭氧（O3）最大8小时滑动平均  0.0  100.0  160.0  215.0  265.0   800.0   \n",
       "4  5.0    粒径小于等于10颗粒物（PM10）24小时平均  0.0   50.0  150.0  250.0  350.0   420.0   \n",
       "5  NaN                        NaN  NaN    NaN    NaN    NaN    NaN     NaN   \n",
       "6  6.0  粒径小于等于2.5颗粒物（PM2.5）24小时平均  0.0   35.0   75.0  115.0  150.0   250.0   \n",
       "\n",
       "    400   500  \n",
       "0    48    60  \n",
       "1  2100  2620  \n",
       "2   750   940  \n",
       "3     -     -  \n",
       "4   500   600  \n",
       "5   NaN   NaN  \n",
       "6   350   500  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "iaqi_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_table=pd.read_excel('iaqi.xls')\n",
    "iaqi_table.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>215.0</td>\n",
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       "      <td>115.0</td>\n",
       "      <td>150.0</td>\n",
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      "text/plain": [
       "     0              空气质量分指数（IAQI）  0.1     50    100    150    200     300  \\\n",
       "0  1.0             一氧化碳（CO）24小时平均  0.0    2.0    4.0   14.0   24.0    36.0   \n",
       "1  2.0            二氧化硫（SO2）24小时平均  0.0   50.0  150.0  475.0  800.0  1600.0   \n",
       "2  3.0            二氧化氮（NO2）24小时平均  0.0   40.0   80.0  180.0  280.0   565.0   \n",
       "3  4.0            臭氧（O3）最大8小时滑动平均  0.0  100.0  160.0  215.0  265.0   800.0   \n",
       "4  5.0    粒径小于等于10颗粒物（PM10）24小时平均  0.0   50.0  150.0  250.0  350.0   420.0   \n",
       "6  6.0  粒径小于等于2.5颗粒物（PM2.5）24小时平均  0.0   35.0   75.0  115.0  150.0   250.0   \n",
       "\n",
       "    400   500  \n",
       "0    48    60  \n",
       "1  2100  2620  \n",
       "2   750   940  \n",
       "3     -     -  \n",
       "4   500   600  \n",
       "6   350   500  "
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   "source": [
    "iaqi_table"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_table=pd.read_excel('iaqi.xls')\n",
    "iaqi_table.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               空气质量分指数（IAQI）    0     50    100    150    200     300   400  \\\n",
       "0             一氧化碳（CO）24小时平均  0.0    2.0    4.0   14.0   24.0    36.0    48   \n",
       "1            二氧化硫（SO2）24小时平均  0.0   50.0  150.0  475.0  800.0  1600.0  2100   \n",
       "2            二氧化氮（NO2）24小时平均  0.0   40.0   80.0  180.0  280.0   565.0   750   \n",
       "3            臭氧（O3）最大8小时滑动平均  0.0  100.0  160.0  215.0  265.0   800.0     -   \n",
       "4    粒径小于等于10颗粒物（PM10）24小时平均  0.0   50.0  150.0  250.0  350.0   420.0   500   \n",
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       "\n",
       "    500  \n",
       "0    60  \n",
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       "3     -  \n",
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  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>粒径小于等于10颗粒物（PM10）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>500</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>粒径小于等于2.5颗粒物（PM2.5）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               空气质量分指数（IAQI）    0     50    100    150    200     300   400  \\\n",
       "0             一氧化碳（CO）24小时平均  0.0    2.0    4.0   14.0   24.0    36.0    48   \n",
       "1            二氧化硫（SO2）24小时平均  0.0   50.0  150.0  475.0  800.0  1600.0  2100   \n",
       "2            二氧化氮（NO2）24小时平均  0.0   40.0   80.0  180.0  280.0   565.0   750   \n",
       "3            臭氧（O3）最大8小时滑动平均  0.0  100.0  160.0  215.0  265.0   800.0   800   \n",
       "4    粒径小于等于10颗粒物（PM10）24小时平均  0.0   50.0  150.0  250.0  350.0   420.0   500   \n",
       "6  粒径小于等于2.5颗粒物（PM2.5）24小时平均  0.0   35.0   75.0  115.0  150.0   250.0   350   \n",
       "\n",
       "    500  \n",
       "0    60  \n",
       "1  2620  \n",
       "2   940  \n",
       "3   800  \n",
       "4   600  \n",
       "6   500  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_answer\n",
    "data_for_answer_copy = data_for_answer.copy()\n",
    "o3_above_800 = data_for_answer_copy.iloc[:,-1]>800\n",
    "data_for_answer_copy[o3_above_800]=0\n",
    "data_for_answer_copy\n",
    "iaqi_table_copy = iaqi_table.copy()\n",
    "iaqi_table_copy.iloc[3,-2:] = 800\n",
    "iaqi_table_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SO2小时平均浓度(μg/m³)</th>\n",
       "      <th>NO2小时平均浓度(μg/m³)</th>\n",
       "      <th>PM10小时平均浓度(μg/m³)</th>\n",
       "      <th>PM2.5小时平均浓度(μg/m³)</th>\n",
       "      <th>O3小时平均浓度(μg/m³)</th>\n",
       "      <th>CO小时平均浓度(mg/m³)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>模型运行日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>2.401510</td>\n",
       "      <td>20.9208</td>\n",
       "      <td>8.17336</td>\n",
       "      <td>5.27729</td>\n",
       "      <td>8.787230</td>\n",
       "      <td>0.124491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>1.448340</td>\n",
       "      <td>14.8144</td>\n",
       "      <td>6.49054</td>\n",
       "      <td>4.33106</td>\n",
       "      <td>12.745300</td>\n",
       "      <td>0.109056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>1.271610</td>\n",
       "      <td>13.9154</td>\n",
       "      <td>6.86679</td>\n",
       "      <td>4.40045</td>\n",
       "      <td>12.229600</td>\n",
       "      <td>0.105957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>0.467429</td>\n",
       "      <td>11.1535</td>\n",
       "      <td>5.25900</td>\n",
       "      <td>3.35261</td>\n",
       "      <td>13.780000</td>\n",
       "      <td>0.101764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>0.574856</td>\n",
       "      <td>13.9989</td>\n",
       "      <td>6.05979</td>\n",
       "      <td>3.59303</td>\n",
       "      <td>9.963330</td>\n",
       "      <td>0.104536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>2.679800</td>\n",
       "      <td>44.8120</td>\n",
       "      <td>8.92721</td>\n",
       "      <td>5.66833</td>\n",
       "      <td>0.333226</td>\n",
       "      <td>0.224216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>2.937120</td>\n",
       "      <td>41.3621</td>\n",
       "      <td>9.00740</td>\n",
       "      <td>5.67797</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.233528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>3.084680</td>\n",
       "      <td>41.5508</td>\n",
       "      <td>9.13525</td>\n",
       "      <td>5.76356</td>\n",
       "      <td>0.024794</td>\n",
       "      <td>0.237719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>4.003400</td>\n",
       "      <td>42.4011</td>\n",
       "      <td>8.24599</td>\n",
       "      <td>5.43220</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.260096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>4.785530</td>\n",
       "      <td>42.6422</td>\n",
       "      <td>7.40284</td>\n",
       "      <td>5.14862</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.268417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25416 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SO2小时平均浓度(μg/m³)  NO2小时平均浓度(μg/m³)  PM10小时平均浓度(μg/m³)  \\\n",
       "模型运行日期                                                              \n",
       "2020-07-23          2.401510           20.9208            8.17336   \n",
       "2020-07-23          1.448340           14.8144            6.49054   \n",
       "2020-07-23          1.271610           13.9154            6.86679   \n",
       "2020-07-23          0.467429           11.1535            5.25900   \n",
       "2020-07-23          0.574856           13.9989            6.05979   \n",
       "...                      ...               ...                ...   \n",
       "2021-07-13          2.679800           44.8120            8.92721   \n",
       "2021-07-13          2.937120           41.3621            9.00740   \n",
       "2021-07-13          3.084680           41.5508            9.13525   \n",
       "2021-07-13          4.003400           42.4011            8.24599   \n",
       "2021-07-13          4.785530           42.6422            7.40284   \n",
       "\n",
       "            PM2.5小时平均浓度(μg/m³)  O3小时平均浓度(μg/m³)  CO小时平均浓度(mg/m³)  \n",
       "模型运行日期                                                            \n",
       "2020-07-23             5.27729         8.787230         0.124491  \n",
       "2020-07-23             4.33106        12.745300         0.109056  \n",
       "2020-07-23             4.40045        12.229600         0.105957  \n",
       "2020-07-23             3.35261        13.780000         0.101764  \n",
       "2020-07-23             3.59303         9.963330         0.104536  \n",
       "...                        ...              ...              ...  \n",
       "2021-07-13             5.66833         0.333226         0.224216  \n",
       "2021-07-13             5.67797         0.007336         0.233528  \n",
       "2021-07-13             5.76356         0.024794         0.237719  \n",
       "2021-07-13             5.43220         0.000928         0.260096  \n",
       "2021-07-13             5.14862         0.001013         0.268417  \n",
       "\n",
       "[25416 rows x 6 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_answer_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_map = np.zeros((7,8),dtype=np.float32)\n",
    "iaqi_map[0] = iaqi_table_copy.iloc[1,1:].to_numpy()\n",
    "iaqi_map[1] = iaqi_table_copy.iloc[2,1:].to_numpy()\n",
    "iaqi_map[2] = iaqi_table_copy.iloc[4,1:].to_numpy()\n",
    "iaqi_map[3] = iaqi_table_copy.iloc[5,1:].to_numpy()\n",
    "iaqi_map[4] = iaqi_table_copy.iloc[3,1:].to_numpy()\n",
    "iaqi_map[5] = iaqi_table_copy.iloc[0,1:].to_numpy()\n",
    "iaqi_map[6] = iaqi_table_copy.columns[1:].to_numpy()\n",
    "iaqi_map = iaqi_map.T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_numpy=data_for_answer_copy.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numpy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 7)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iaqi_map.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_map = iaqi_map.reshape(1,7,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_numpy = data_numpy[:,:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6, 1)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numpy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 7, 8)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iaqi_map.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = data_numpy > iaqi_map[:,:-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6, 8)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=index.argmax(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 3, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.40151 ],\n",
       "       [20.9208  ],\n",
       "       [ 8.17336 ],\n",
       "       [ 5.27729 ],\n",
       "       [ 8.78723 ],\n",
       "       [ 0.124491]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numpy[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>空气质量分指数（IAQI）</th>\n",
       "      <th>0</th>\n",
       "      <th>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>300</th>\n",
       "      <th>400</th>\n",
       "      <th>500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一氧化碳（CO）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>二氧化硫（SO2）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>2620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>二氧化氮（NO2）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>750</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>臭氧（O3）最大8小时滑动平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>粒径小于等于10颗粒物（PM10）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>500</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>粒径小于等于2.5颗粒物（PM2.5）24小时平均</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>350</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               空气质量分指数（IAQI）    0     50    100    150    200     300   400  \\\n",
       "0             一氧化碳（CO）24小时平均  0.0    2.0    4.0   14.0   24.0    36.0    48   \n",
       "1            二氧化硫（SO2）24小时平均  0.0   50.0  150.0  475.0  800.0  1600.0  2100   \n",
       "2            二氧化氮（NO2）24小时平均  0.0   40.0   80.0  180.0  280.0   565.0   750   \n",
       "3            臭氧（O3）最大8小时滑动平均  0.0  100.0  160.0  215.0  265.0   800.0   800   \n",
       "4    粒径小于等于10颗粒物（PM10）24小时平均  0.0   50.0  150.0  250.0  350.0   420.0   500   \n",
       "6  粒径小于等于2.5颗粒物（PM2.5）24小时平均  0.0   35.0   75.0  115.0  150.0   250.0   350   \n",
       "\n",
       "    500  \n",
       "0    60  \n",
       "1  2620  \n",
       "2   940  \n",
       "3   800  \n",
       "4   600  \n",
       "6   500  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iaqi_table_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = data_numpy[0]\n",
    "res=data_test > iaqi_map[:,:-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ True,  True,  True,  True,  True,  True,  True, False],\n",
       "        [False, False, False, False,  True, False, False, False],\n",
       "        [False, False, False,  True, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False]]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,\n",
       "         0.00e+00, 5.00e+01],\n",
       "        [4.00e+01, 5.00e+01, 3.50e+01, 1.00e+02, 2.00e+00, 5.00e+01,\n",
       "         1.50e+02, 8.00e+01],\n",
       "        [1.50e+02, 7.50e+01, 1.60e+02, 4.00e+00, 1.00e+02, 4.75e+02,\n",
       "         1.80e+02, 2.50e+02],\n",
       "        [1.15e+02, 2.15e+02, 1.40e+01, 1.50e+02, 8.00e+02, 2.80e+02,\n",
       "         3.50e+02, 1.50e+02],\n",
       "        [2.65e+02, 2.40e+01, 2.00e+02, 1.60e+03, 5.65e+02, 4.20e+02,\n",
       "         2.50e+02, 8.00e+02],\n",
       "        [3.60e+01, 3.00e+02, 2.10e+03, 7.50e+02, 5.00e+02, 3.50e+02,\n",
       "         8.00e+02, 4.80e+01],\n",
       "        [4.00e+02, 2.62e+03, 9.40e+02, 6.00e+02, 5.00e+02, 8.00e+02,\n",
       "         6.00e+01, 5.00e+02]]], dtype=float32)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iaqi_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "iaqi_map.reshape(7,8)\n",
    "iaqi_numpy = iaqi_table_copy.iloc[:,1:].to_numpy()\n",
    "iaqi_numpy.shape\n",
    "iaqi_numpy = iaqi_numpy.reshape(1,6,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[False, False,  True,  True,  True,  True,  True,  True],\n",
       "        [False,  True,  True,  True,  True,  True,  True,  True],\n",
       "        [False,  True,  True,  True,  True,  True,  True,  True],\n",
       "        [False,  True,  True,  True,  True,  True,  True,  True],\n",
       "        [False,  True,  True,  True,  True,  True,  True,  True],\n",
       "        [False,  True,  True,  True,  True,  True,  True,  True]]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test < iaqi_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6, 8)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = data_numpy < iaqi_numpy\n",
    "index.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = index.argmax(-1)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_map = iaqi_table_copy.columns[1:].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  50., 100., 150., 200., 300., 400., 500.], dtype=float32)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_map.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = score_map[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25416, 6)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(score>=500).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x24f9f3c5d30>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3b9048>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1198>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1470>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c025c0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c02898>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c189e8>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c18cc0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c2ce10>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3d128>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3b278>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3b550>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x24f9f3b9320>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3b95f8>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1748>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1a20>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c02b70>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c02e48>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c18f98>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c2c2b0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3d400>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3d6d8>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3b828>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3bb00>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x24f9f3c5ac8>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3b9da0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c02208>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c18630>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c2ca58>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3de80>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x24f9f3b98d0>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1cf8>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c18160>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c2c588>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3d9b0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3bdd8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x24f9f3b9ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9f3e1fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c18438>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c2c860>,\n",
       "  <matplotlib.lines.Line2D at 0x24fa4c3dc88>,\n",
       "  <matplotlib.lines.Line2D at 0x24f9ecf80f0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\software\\Anaconda\\envs\\torch19\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "score[score>=500] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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